Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Models
Fengzhu Zeng, Wei Guang Gao · 2023
Few-shot or zero-shot fact verification only relies on a few or no labeled training examples.In this paper, we propose a novel method called ProToCo, to Prompt pre-trained language models (PLMs) To be Consistent, for improving the factuality assessment capability of PLMs in the few-shot and zero-shot settings.Given a claim-evidence pair, ProToCo generates multiple variants of the claim with different relations and frames a simple consistency mechanism as constraints for making compatible predictions across these variants.We update PLMs by using parameter-efficient fine-tuning (PEFT), leading to more accurate predictions in few-shot and zero-shot fact verification tasks.Our experiments on three public verification datasets show that ProToCo significantly outperforms state-of-the-art few-shot fact verification baselines.With a small number of unlabeled instances, ProToCo also outperforms the strong zero-shot learner T0 on zero-shot verification.Compared to large PLMs using incontext learning (ICL) method, ProToCo outperforms OPT-30B and the Self-Consistencyenabled OPT-6.7Bmodel in both few-and zeroshot settings.Original Input: Suppose {Evidence}.Can we infer {Claim}?Evidence： Coronavirus disease 2019 is a zoonotic infectious disease caused by severe acute respiratory syndrome coronavirus 2. Claim:The Coronavirus disease 2019 has a zoonotic origin.